| Name | mmh3 JSON |
| Version |
4.1.0
JSON |
| download |
| home_page | |
| Summary | Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions. |
| upload_time | 2024-01-09 06:46:04 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | |
| license | MIT License Copyright (c) 2011-2023 Hajime Senuma Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
utility
hash
murmurhash
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# mmh3
[](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster)
[](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master)
[](https://pypi.org/project/mmh3/)
[](https://pypi.org/project/mmh3/)
[](https://opensource.org/license/mit/)
[](https://pepy.tech/project/mmh3?versions=*&versions=4.*&versions=3.*&versions=2.*)
[](https://pepy.tech/project/mmh3?versions=*&versions=4.*&versions=3.*&versions=2.*)
mmh3 is a Python extension for [MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby.
Combined with probabilistic techniques like a [Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), [MinHash](https://en.wikipedia.org/wiki/MinHash), and [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing.
Another common use of mmh3 is to [calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) used by [Shodan](https://www.shodan.io), the world's first IoT search engine.
## How to use
### Install
```shell
pip install mmh3 # for macOS, use "pip3 install mmh3" and python3
```
### Simple functions
Quickstart:
```shell
>>> import mmh3
>>> mmh3.hash("foo") # returns a 32-bit signed int
-156908512
>>> mmh3.hash("foo", 42) # uses 42 as a seed
-1322301282
>>> mmh3.hash("foo", signed=False) # returns a 32-bit unsigned int
4138058784
```
Other functions:
```shell
>>> mmh3.hash64("foo") # two 64 bit signed ints (by using the 128-bit algorithm as its backend)
(-2129773440516405919, 9128664383759220103)
>>> mmh3.hash64("foo", signed=False) # two 64 bit unsigned ints
(16316970633193145697, 9128664383759220103)
>>> mmh3.hash128("foo", 42) # 128 bit unsigned int
215966891540331383248189432718888555506
>>> mmh3.hash128("foo", 42, signed=True) # 128 bit signed int
-124315475380607080215185174712879655950
>>> mmh3.hash_bytes("foo") # 128 bit value as bytes
'aE\xf5\x01W\x86q\xe2\x87}\xba+\xe4\x87\xaf~'
>>> import numpy as np
>>> a = np.zeros(2 ** 32, dtype=np.int8)
>>> mmh3.hash_bytes(a)
b'V\x8f}\xad\x8eNM\xa84\x07FU\x9c\xc4\xcc\x8e'
```
Beware that `hash64` returns **two** values, because it uses the 128-bit version of MurmurHash3 as its backend.
`hash_from_buffer` hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as `numpy.ndarray`.
```shell
>>> mmh3.hash_from_buffer(numpy.random.rand(100))
-2137204694
>>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False)
3812874078
```
`hash64`, `hash128`, and `hash_bytes` have the third argument for architecture optimization (keyword arg: `x64arch`). Use True for x64 and False for x86 (default: True):
```shell
>>> mmh3.hash64("foo", 42, True)
(-840311307571801102, -6739155424061121879)
```
### `hashlib`-style hashers
`mmh3` implements hashers whose interfaces are similar to `hashlib` in the standard library: `mmh3_32()` for 32 bit hashing, `mmh3_x64_128()` for 128 bit hashing optimized for x64 architectures, and `mmh3_x86_128()` for 128 bit hashing optimized for x86 architectures.
In addition to the standard `digest()` method, each hasher has `sintdigest()`, which returns a signed integer, and `uintdigest()`, which returns an unsigned integer. 128 bit hashers also have `stupledigest()` and `utupledigest()` which return two 64 bit integers.
Note that as of version 4.1.0, the implementation is still experimental and its performance can be unsatisfactory (especially `mmh3_x86_128()`). Also, `hexdigest()` is not supported. Use `digest().hex()` instead.
```shell
>>> import mmh3
>>> hasher = mmh3.mmh3_x64_128(seed=42)
>>> hasher.update(b"foo")
>>> hasher.update(b"bar")
>>> hasher.update("foo") # str inputs are not allowed for hashers
TypeError: Strings must be encoded before hashing
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
>>> hasher.digest()
b'\x82_n\xdd \xac\xb6j\xef\x99\xb1e\xc4\n\xc9\xfd'
>>> hasher.sintdigest() # 128 bit signed int
-2943813934500665152301506963178627198
>>> hasher.uintdigest() # 128 bit unsigned int
337338552986437798311073100468589584258
>>> hasher.stupledigest() # two 64 bit signed ints
(7689522670935629698, -159584473158936081)
>>> hasher.utupledigest() # two 64 bit unsigned ints
(7689522670935629698, 18287159600550615535)
```
## Changelog
### 4.1.0 (2024-01-09)
* Add support for Python 3.12.
* Change the project structure to fix issues when using Bazel (<https://github.com/hajimes/mmh3/issues/50>).
* Fix incorrect type hints (<https://github.com/hajimes/mmh3/issues/51>).
* Fix invalid results on s390x when the arg `x64arch` of `hash64` or `hash_bytes` is set to `False` (<https://github.com/hajimes/mmh3/issues/52>).
### 4.0.1 (2023-07-14)
* Fix incorrect type hints.
* Refactor the project structure (<https://github.com/hajimes/mmh3/issues/48>).
### 4.0.0 (2023-05-22)
* Add experimental support for `hashlib`-compliant hasher classes (<https://github.com/hajimes/mmh3/issues/39>). Note that they are not yet fully tuned for performance.
* Add support for type hints (<https://github.com/hajimes/mmh3/issues/44>).
* Add wheels for more platforms (`musllinux`, `s390x`, `win_arm64`, and `macosx_universal2`).
* Drop support for Python 3.7, as it will reach the end of life on 2023-06-27.
* Switch license from CC0 to MIT (<https://github.com/hajimes/mmh3/issues/43>).
* Add a code of conduct (the ACM Code of Ethics and Professional Conduct).
* Backward incompatible changes:
* A hash function now returns the same value under big-endian platforms as that under little-endian ones (<https://github.com/hajimes/mmh3/issues/47>).
* Remove the `__version__` constant from the module (<https://github.com/hajimes/mmh3/issues/42>). Use `importlib.metadata` instead.
See [CHANGELOG.md](./docs/CHANGELOG.md) for the complete changelog.
## License
[MIT](./LICENSE), unless otherwise noted within a file.
## Known Issues
### Getting different results from other MurmurHash3-based libraries
By default, mmh3 returns **signed** values for 32-bit and 64-bit versions and **unsigned** values for `hash128`, due to historical reasons. Please use the keyword argument `signed` to obtain a desired result.
From version 4.0.0, `mmh3` returns the same value under big-endian platforms
as that under little-endian ones, while the original C++ library is endian-sensitive. If you need to obtain the original-compliant results under big-endian environments, please use version 3.*.
For compatibility with [Google Guava (Java)](https://github.com/google/guava), see <https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation>.
For compatibility with [murmur3 (Go)](https://pkg.go.dev/github.com/spaolacci/murmur3), see <https://github.com/hajimes/mmh3/issues/46>.
### Unexpected results when given non 32-bit seeds
Version 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit.
```shell
>>> mmh3.hash("aaaa", -1756908916) # signed representation for 0x9747b28c
1519878282
>>> mmh3.hash("aaaa", 2538058380) # unsigned representation for 0x9747b28c
1519878282
```
Be careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values.
```shell
>>> mmh3.hash("foo", 2 ** 33)
-156908512
>>> mmh3.hash("foo", 2 ** 34)
-156908512
```
## Contributing Guidelines
See [CONTRIBUTING.md](./docs/CONTRIBUTING.md).
## Authors
MurmurHash3 was originally developed by Austin Appleby and distributed under public domain [https://github.com/aappleby/smhasher](https://github.com/aappleby/smhasher).
Ported and modified for Python by Hajime Senuma.
## See also
### Tutorials (High-Performance Computing)
The following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing.
* Chapter 11: *Using Less Ram* in Micha Gorelick and Ian Ozsvald. 2014. *High Performance Python: Practical Performant Programming for Humans*. O'Reilly Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595).
* 2nd edition of the above (2020). [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026).
* Max Burstein. February 2, 2013. *[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)*.
* Duke University. April 14, 2016. *[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)*.
* Bugra Akyildiz. August 24, 2016. *[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)*. KDnuggets.
### Tutorials (Internet of Things)
[Shodan](https://www.shodan.io), the world's first [IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses MurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon) (icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows Shodan's convention.
[Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) is useful for OSINT and cybersecurity activities.
* Jan Kopriva. April 19, 2021. *[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)*. SANS Internet Storm Center.
* Nikhil Panwar. May 2, 2022. *[Using Favicons to Discover Phishing & Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)*. Bolster.
* Faradaysec. July 25, 2022. *[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)*. Faraday Security.
* Debjeet. August 2, 2022. *[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)*. Payatu.
### Similar libraries
* <https://github.com/wc-duck/pymmh3>: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani)
* <https://github.com/escherba/python-cityhash>: Python bindings for CityHash (Eugene Scherba)
* <https://github.com/veelion/python-farmhash>: Python bindigs for FarmHash (Veelion Chong)
* <https://github.com/escherba/python-metrohash>: Python bindings for MetroHash (Eugene Scherba)
* <https://github.com/ifduyue/python-xxhash>: Python bindings for xxHash (Yue Du)
Raw data
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"author_email": "Hajime Senuma <hajime.senuma@gmail.com>",
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"description": "# mmh3\n[](https://github.com/hajimes/mmh3/actions?query=workflow%3ASuper-Linter+branch%3Amaster)\n[](https://github.com/hajimes/mmh3/actions/workflows/build.yml?branch=master)\n[](https://pypi.org/project/mmh3/)\n[](https://pypi.org/project/mmh3/)\n[](https://opensource.org/license/mit/)\n[](https://pepy.tech/project/mmh3?versions=*&versions=4.*&versions=3.*&versions=2.*)\n[](https://pepy.tech/project/mmh3?versions=*&versions=4.*&versions=3.*&versions=2.*)\n\nmmh3 is a Python extension for [MurmurHash (MurmurHash3)](https://en.wikipedia.org/wiki/MurmurHash), a set of fast and robust non-cryptographic hash functions invented by Austin Appleby.\n\nCombined with probabilistic techniques like a [Bloom filter](https://en.wikipedia.org/wiki/Bloom_filter), [MinHash](https://en.wikipedia.org/wiki/MinHash), and [feature hashing](https://en.wikipedia.org/wiki/Feature_hashing), mmh3 allows you to develop high-performance systems in fields such as data mining, machine learning, and natural language processing.\n\nAnother common use of mmh3 is to [calculate favicon hashes](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) used by [Shodan](https://www.shodan.io), the world's first IoT search engine.\n\n## How to use\n### Install\n```shell\npip install mmh3 # for macOS, use \"pip3 install mmh3\" and python3\n```\n\n### Simple functions\nQuickstart:\n```shell\n>>> import mmh3\n>>> mmh3.hash(\"foo\") # returns a 32-bit signed int\n-156908512\n>>> mmh3.hash(\"foo\", 42) # uses 42 as a seed\n-1322301282\n>>> mmh3.hash(\"foo\", signed=False) # returns a 32-bit unsigned int\n4138058784\n```\n\nOther functions:\n```shell\n>>> mmh3.hash64(\"foo\") # two 64 bit signed ints (by using the 128-bit algorithm as its backend)\n(-2129773440516405919, 9128664383759220103)\n>>> mmh3.hash64(\"foo\", signed=False) # two 64 bit unsigned ints\n(16316970633193145697, 9128664383759220103)\n>>> mmh3.hash128(\"foo\", 42) # 128 bit unsigned int\n215966891540331383248189432718888555506\n>>> mmh3.hash128(\"foo\", 42, signed=True) # 128 bit signed int\n-124315475380607080215185174712879655950\n>>> mmh3.hash_bytes(\"foo\") # 128 bit value as bytes\n'aE\\xf5\\x01W\\x86q\\xe2\\x87}\\xba+\\xe4\\x87\\xaf~'\n>>> import numpy as np\n>>> a = np.zeros(2 ** 32, dtype=np.int8)\n>>> mmh3.hash_bytes(a)\nb'V\\x8f}\\xad\\x8eNM\\xa84\\x07FU\\x9c\\xc4\\xcc\\x8e'\n```\n\nBeware that `hash64` returns **two** values, because it uses the 128-bit version of MurmurHash3 as its backend.\n\n`hash_from_buffer` hashes byte-likes without memory copying. The method is suitable when you hash a large memory-view such as `numpy.ndarray`.\n\n```shell\n>>> mmh3.hash_from_buffer(numpy.random.rand(100))\n-2137204694\n>>> mmh3.hash_from_buffer(numpy.random.rand(100), signed=False)\n3812874078\n```\n\n`hash64`, `hash128`, and `hash_bytes` have the third argument for architecture optimization (keyword arg: `x64arch`). Use True for x64 and False for x86 (default: True):\n\n```shell\n>>> mmh3.hash64(\"foo\", 42, True) \n(-840311307571801102, -6739155424061121879)\n```\n\n### `hashlib`-style hashers\n`mmh3` implements hashers whose interfaces are similar to `hashlib` in the standard library: `mmh3_32()` for 32 bit hashing, `mmh3_x64_128()` for 128 bit hashing optimized for x64 architectures, and `mmh3_x86_128()` for 128 bit hashing optimized for x86 architectures.\n\nIn addition to the standard `digest()` method, each hasher has `sintdigest()`, which returns a signed integer, and `uintdigest()`, which returns an unsigned integer. 128 bit hashers also have `stupledigest()` and `utupledigest()` which return two 64 bit integers.\n\nNote that as of version 4.1.0, the implementation is still experimental and its performance can be unsatisfactory (especially `mmh3_x86_128()`). Also, `hexdigest()` is not supported. Use `digest().hex()` instead.\n\n```shell\n>>> import mmh3\n>>> hasher = mmh3.mmh3_x64_128(seed=42)\n>>> hasher.update(b\"foo\")\n>>> hasher.update(b\"bar\")\n>>> hasher.update(\"foo\") # str inputs are not allowed for hashers\nTypeError: Strings must be encoded before hashing\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\n>>> hasher.digest()\nb'\\x82_n\\xdd \\xac\\xb6j\\xef\\x99\\xb1e\\xc4\\n\\xc9\\xfd'\n>>> hasher.sintdigest() # 128 bit signed int\n-2943813934500665152301506963178627198\n>>> hasher.uintdigest() # 128 bit unsigned int\n337338552986437798311073100468589584258\n>>> hasher.stupledigest() # two 64 bit signed ints\n(7689522670935629698, -159584473158936081)\n>>> hasher.utupledigest() # two 64 bit unsigned ints\n(7689522670935629698, 18287159600550615535)\n```\n\n## Changelog\n### 4.1.0 (2024-01-09)\n* Add support for Python 3.12.\n* Change the project structure to fix issues when using Bazel (<https://github.com/hajimes/mmh3/issues/50>).\n* Fix incorrect type hints (<https://github.com/hajimes/mmh3/issues/51>).\n* Fix invalid results on s390x when the arg `x64arch` of `hash64` or `hash_bytes` is set to `False` (<https://github.com/hajimes/mmh3/issues/52>).\n\n### 4.0.1 (2023-07-14)\n* Fix incorrect type hints.\n* Refactor the project structure (<https://github.com/hajimes/mmh3/issues/48>).\n\n### 4.0.0 (2023-05-22)\n* Add experimental support for `hashlib`-compliant hasher classes (<https://github.com/hajimes/mmh3/issues/39>). Note that they are not yet fully tuned for performance.\n* Add support for type hints (<https://github.com/hajimes/mmh3/issues/44>).\n* Add wheels for more platforms (`musllinux`, `s390x`, `win_arm64`, and `macosx_universal2`).\n* Drop support for Python 3.7, as it will reach the end of life on 2023-06-27.\n* Switch license from CC0 to MIT (<https://github.com/hajimes/mmh3/issues/43>).\n* Add a code of conduct (the ACM Code of Ethics and Professional Conduct).\n* Backward incompatible changes:\n * A hash function now returns the same value under big-endian platforms as that under little-endian ones (<https://github.com/hajimes/mmh3/issues/47>).\n * Remove the `__version__` constant from the module (<https://github.com/hajimes/mmh3/issues/42>). Use `importlib.metadata` instead.\n\nSee [CHANGELOG.md](./docs/CHANGELOG.md) for the complete changelog.\n\n## License\n[MIT](./LICENSE), unless otherwise noted within a file.\n\n## Known Issues\n### Getting different results from other MurmurHash3-based libraries\nBy default, mmh3 returns **signed** values for 32-bit and 64-bit versions and **unsigned** values for `hash128`, due to historical reasons. Please use the keyword argument `signed` to obtain a desired result.\n\nFrom version 4.0.0, `mmh3` returns the same value under big-endian platforms\n as that under little-endian ones, while the original C++ library is endian-sensitive. If you need to obtain the original-compliant results under big-endian environments, please use version 3.*.\n\nFor compatibility with [Google Guava (Java)](https://github.com/google/guava), see <https://stackoverflow.com/questions/29932956/murmur3-hash-different-result-between-python-and-java-implementation>.\n\nFor compatibility with [murmur3 (Go)](https://pkg.go.dev/github.com/spaolacci/murmur3), see <https://github.com/hajimes/mmh3/issues/46>.\n\n### Unexpected results when given non 32-bit seeds\nVersion 2.4 changed the type of seeds from signed 32-bit int to unsigned 32-bit int. The resulting values with signed seeds still remain the same as before, as long as they are 32-bit.\n\n```shell\n>>> mmh3.hash(\"aaaa\", -1756908916) # signed representation for 0x9747b28c\n1519878282\n>>> mmh3.hash(\"aaaa\", 2538058380) # unsigned representation for 0x9747b28c\n1519878282\n```\n\nBe careful so that these seeds do not exceed 32-bit. Unexpected results may happen with invalid values.\n\n```shell\n>>> mmh3.hash(\"foo\", 2 ** 33)\n-156908512\n>>> mmh3.hash(\"foo\", 2 ** 34)\n-156908512\n```\n\n## Contributing Guidelines\nSee [CONTRIBUTING.md](./docs/CONTRIBUTING.md).\n\n## Authors\nMurmurHash3 was originally developed by Austin Appleby and distributed under public domain [https://github.com/aappleby/smhasher](https://github.com/aappleby/smhasher).\n\nPorted and modified for Python by Hajime Senuma.\n\n## See also\n### Tutorials (High-Performance Computing)\nThe following textbooks and tutorials are great sources to learn how to use mmh3 (and other hash algorithms in general) for high-performance computing.\n\n* Chapter 11: *Using Less Ram* in Micha Gorelick and Ian Ozsvald. 2014. *High Performance Python: Practical Performant Programming for Humans*. O'Reilly Media. [ISBN: 978-1-4493-6159-4](https://www.amazon.com/dp/1449361595).\n * 2nd edition of the above (2020). [ISBN: 978-1492055020](https://www.amazon.com/dp/1492055026).\n* Max Burstein. February 2, 2013. *[Creating a Simple Bloom Filter](http://www.maxburstein.com/blog/creating-a-simple-bloom-filter/)*.\n* Duke University. April 14, 2016. *[Efficient storage of data in memory](http://people.duke.edu/~ccc14/sta-663-2016/20B_Big_Data_Structures.html)*.\n* Bugra Akyildiz. August 24, 2016. *[A Gentle Introduction to Bloom Filter](https://www.kdnuggets.com/2016/08/gentle-introduction-bloom-filter.html)*. KDnuggets.\n\n### Tutorials (Internet of Things)\n[Shodan](https://www.shodan.io), the world's first [IoT](https://en.wikipedia.org/wiki/Internet_of_things) search engine, uses MurmurHash3 hash values for [favicons](https://en.wikipedia.org/wiki/Favicon) (icons associated with web pages). [ZoomEye](https://www.zoomeye.org) follows Shodan's convention.\n[Calculating these values with mmh3](https://gist.github.com/yehgdotnet/b9dfc618108d2f05845c4d8e28c5fc6a) is useful for OSINT and cybersecurity activities.\n\n* Jan Kopriva. April 19, 2021. *[Hunting phishing websites with favicon hashes](https://isc.sans.edu/diary/Hunting+phishing+websites+with+favicon+hashes/27326)*. SANS Internet Storm Center.\n* Nikhil Panwar. May 2, 2022. *[Using Favicons to Discover Phishing & Brand Impersonation Websites](https://bolster.ai/blog/how-to-use-favicons-to-find-phishing-websites)*. Bolster.\n* Faradaysec. July 25, 2022. *[Understanding Spring4Shell: How used is it?](https://faradaysec.com/understanding-spring4shell/)*. Faraday Security.\n* Debjeet. August 2, 2022. *[How To Find Assets Using Favicon Hashes](https://payatu.com/blog/favicon-hash/)*. Payatu.\n\n### Similar libraries\n* <https://github.com/wc-duck/pymmh3>: mmh3 in pure python (Fredrik Kihlander and Swapnil Gusani)\n* <https://github.com/escherba/python-cityhash>: Python bindings for CityHash (Eugene Scherba)\n* <https://github.com/veelion/python-farmhash>: Python bindigs for FarmHash (Veelion Chong)\n* <https://github.com/escherba/python-metrohash>: Python bindings for MetroHash (Eugene Scherba)\n* <https://github.com/ifduyue/python-xxhash>: Python bindings for xxHash (Yue Du)\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2011-2023 Hajime Senuma Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
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